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Agronomy
Special Issues
Smart Pest Control for Building Farm Resilience
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Smart Pest Control for Building Farm Resilience

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Pest and Disease Management".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 730

Special Issue Editors

Dr. Jiaxing Xie

E-Mail Website
Guest Editor
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Interests: agricultural information acquisition and monitoring; smart agriculture; phenotypic information research
Dr. Daozong Sun

E-Mail Website
Guest Editor
College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Interests: agriculture

Special Issue Information

Dear Colleagues,

Agriculture stands at the forefront of the fight against climate change, with pest management playing a crucial role in ensuring food security and environmental sustainability. Traditional pest control methods, which often rely heavily on chemical pesticides, have demonstrated short-term effectiveness but pose significant long-term risks. These risks include environmental degradation, the development of pest resistance, and adverse effects on non-target species. In response to these challenges, the concept of Smart Pest Control has emerged as a transformative solution. It integrates advanced technologies with sustainable practices to build resilience in farming systems. Smart Pest Control offers numerous benefits, such as reduced pesticide usage, lower costs, and enhanced environmental sustainability. By leveraging cutting-edge technologies like deep learning and large language models, farmers can develop intelligent pest monitoring systems that are adaptable to diverse and complex environments. These systems enable more precise and efficient pest management, minimizing the need for chemical interventions and maximizing ecological benefits. Looking ahead, future research should focus on scaling these technologies, improving their accessibility, and exploring their potential across a wide range of agricultural contexts. This will ensure that Smart Pest Control continues to evolve as a powerful tool for sustainable farming and climate resilience.

Dr. Jiaxing Xie
Dr. Daozong Sun
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pest management
  • environmental sustainability
  • pest resistance
  • smart pest control
  • deep learning
  • large language models
  • intelligent pest monitoring systems
  • sustainable farming
  • resilience

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Published Papers (2 papers)

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Research

21 pages, 5571 KiB  
Article
YOLOv11-RDTNet: A Lightweight Model for Citrus Pest and Disease Identification Based on an Improved YOLOv11n
by Qiufang Dai, Shiyao Liang, Zhen Li, Shilei Lyu, Xiuyun Xue, Shuran Song, Ying Huang, Shaoyu Zhang and Jiaheng Fu
Agronomy 2025, 15(5), 1252; https://doi.org/10.3390/agronomy15051252 - 21 May 2025
Abstract
Citrus pests and diseases severely impact fruit yield and quality. However, existing object detection models face limitations in complex backgrounds, target occlusion, and small target recognition, and they struggle to be efficiently deployed on resource-constrained devices. To address these issues, this study proposes [...] Read more.
Citrus pests and diseases severely impact fruit yield and quality. However, existing object detection models face limitations in complex backgrounds, target occlusion, and small target recognition, and they struggle to be efficiently deployed on resource-constrained devices. To address these issues, this study proposes a lightweight pest and disease detection model, YOLOv11-RDTNet, based on the improved YOLOv11n. This model integrates multi-scale features and attention mechanisms to enhance recognition performance in complex scenarios, while adopting a lightweight design to reduce computational costs and improve deployment adaptability. The model introduces three key enhancement features: First, shallow RFD (SRFD) and deep RFD (DRFD) downsampling modules replace traditional convolution modules, improving image feature extraction accuracy and robustness. Second, the Dynamic Group Shuffle Transformer (DGST) module replaces the original C3k2 module, reducing the model’s parameter count and computational demand, further enhancing efficiency and performance. Lastly, the lightweight Task Align Dynamic Detection Head (TADDH) replaces the original detection head, significantly reducing the parameter count and improving accuracy in small-object detection. After processing the collected images, we obtained 1382 images and constructed a dataset containing five types of citrus pests and diseases: anthracnose, canker, yellow vein disease, coal pollution disease, and leaf miner moth. We applied data augmentation on the dataset and conducted experimental validation. Experimental results showed that the YOLOv11-RDTNet model had a parameter count of 1.54 MB, an mAP50 of 87.0%, and a model size of 3.4 MB. Compared to the original YOLOv11 model, the YOLOv11-RDTNet model reduced the parameter count by 40.3%, improved mAP50 by 4.8%, and reduced the model size from 5.5 MB to 3.4 MB. This model not only improved detection accuracy and reduced computational load but also achieved a balance in performance, size, and speed, making it more suitable for deployment on mobile devices. Additionally, the research findings provided an effective tool for citrus pest and disease detection with small sample sizes, offering valuable insights for citrus pest and disease detection in agricultural practices. Full article
(This article belongs to the Special Issue Smart Pest Control for Building Farm Resilience)
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23 pages, 15091 KiB  
Article
Research on the Application Effect and Parameter Optimization of 3HW36 Mountain Orchard Rail-Mounted Wind-Driven Plant Protection Equipment in Fruit Tree Canopy
by Xiuyun Xue, Maofeng Bu, Zhen Li, Yichi Li, Yifu Liu, Wenqi Ye, Chengle Huang and Shilei Lyu
Agronomy 2025, 15(4), 781; https://doi.org/10.3390/agronomy15040781 - 22 Mar 2025
Viewed by 333
Abstract
This study presents a systematic optimization framework of 3HW36 Mountain Orchard Rail-Mounted Wind-Driven Plant Protection Equipment through integrated computational fluid dynamics (CFD), wind field validation, and field experiments. The CFD model demonstrated high fidelity with experimental measurements, achieving a mean absolute percentage error [...] Read more.
This study presents a systematic optimization framework of 3HW36 Mountain Orchard Rail-Mounted Wind-Driven Plant Protection Equipment through integrated computational fluid dynamics (CFD), wind field validation, and field experiments. The CFD model demonstrated high fidelity with experimental measurements, achieving a mean absolute percentage error of 9.2% across 15 sampling points and resolving critical airflow–canopy interactions through a novel porous media approach. Field trials in Fujian citrus orchards quantified the following optimal operational parameters: 29 m/s airflow velocity (23% velocity attenuation through mid-canopy), 15° blower pitch angles (89.6% upper-middle canopy coverage), and 0.5 m/s railcar speed. The equipment’s terrain adaptability was validated through sustained post-canopy velocities (>6.4 m/s) and 62% momentum retention at 4.2 m downstream, addressing critical limitations in mountainous pesticide application. These findings establish a replicable protocol for precision canopy management, balancing agrochemical efficacy with environmental stewardship in complex orchard ecosystems. Full article
(This article belongs to the Special Issue Smart Pest Control for Building Farm Resilience)
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